Judicial Decisions on Patent Validity

Cases at the German Federal Patent Court, 2000 - 2020

Judicial Decision Making in Patent Litigation

In the legal and political science literatures, there has been an ongoing debate about what drives judicial decision making for more than 100 years (Cross 2003). The legalist (or legal rationalist or legal formalist) tradition sees the judge as a vehicle executing the law. The realist (or behaviouralist) tradition sees the law as incomplete and as leaving decision making degrees of freedom to judges. Judges, then, respond to attitudinal preferences or strategic incentives (Segal 2001, Epstein & Knight 2013). This debate is strongly biased towards the USA and especially its Supreme Court, where politically controversial cases and partisan decision making are frequent.

While the realist approach has demonstrated its relevance in the setting of politicized US courts in many empirical studies (e.g. Segal & Cover 1989), patent law differs from other fields of law due to its highly technical nature. To come to a satisfactory conclusion in a patent dispute, in-depth technical knowledge relating to the case at hand is usually required in addition to legal knowledge. These differences also change the appropriateness of legalist and realist accounts of judicial decision making. At least three factors favour a legalist model of decision making in patent litigation: (1) a complete description of the facts of the case exists in the form of the patent document, (2) there are standard legal techniques (such as the fictious ‘person skilled in the art’ or criteria for validity) to derive a conclusion from the facts of the case and the law, and (3) the existence of political incentives for the judiciary in the outcomes of most patent disputes is less obvious. Given such technical, legal, and procedural constraints, a reasonable expectation is that judicial degrees of freedom are minimal:

\(H_{l}\): Under a legalist model of judicial decision making, there should be no systematic between-judge variation across comparable cases.

Despite ostensibly transparent criteria and comprehensive technical information, the reality of judicial decision making in the context of patent litigation is however often different: First, there is often deliberate uncertainty in the technical specification contained in the patent document to broaden its scope of protection (Mullally 2009), making the decision less formulaic. Second, specific legal constructions are often applied differently in different legal contexts or schools of thought, such as was the case with the ‘doctrine of equivalents’ in the famous Epilady controversy (Hatter 1994). And third, patent judges have been found to diverge in their substantive interpretations of what constitutes a patent (Lazega et al. 2017). Given these factors, an alternative hypothesis

\(H_{r}\): Under a realist model of judicial decision making, there are judge degrees of freedom, allowing for between-judge variation in decision making even for comparable cases.

The goal of this paper is to empirically test \(H_l\) against \(H_r\) within a single court, the German Federal Patent Court. Given this goal, a core task is to define what constitutes ‘comparable cases’. We here investigate three factors to control for: First, we control for the timepoint of the decision, as the observation period stretches over a period of relative turmoil in the global patenting system, which might have a systematic impact on case outcomes. Second, technology classification, as different technologies might be subject to different industry dynamics, which in turn might tip the scales systematically in the plaintiff’s or defendant’s direction. And third, senate affiliation, as specific senates might have developed their own explicit or implicit rules.

Data on Validity Cases at the Bundespatentgericht

Code

using Model
using Dictionaries, SplitApplyCombine
using DataFramesMeta, Dates

The data explored here were obtained from the BPatG online repository containing all of the court’s decisions since 2000. Initially, all 30,000 documents were downloaded. Among these, nullity decisions were selected and filtered down to only verdict documents (excluding, e.g., additional decisions on compensation etc.). This left around 1,200 documents, from which we extracted key metadata.

decisions = Model.loaddata("../data/processed/json_augmented");

# Get a look at the data for an example decision
first(decisions)

Ruling 1 Ni 4/99 (EU) on EP0389008
Date of decision: 26 September, 2000
Decided by: 1. Senate (Hacker, Vogel, Henkel, Maier, van_Raden)
Outcome: partially annulled

# count of observations in the sample
length(decisions)

1227

An outcome is coded as 0 if the claim is dismissed and as 1 if the patent is fully or partially annulled:

# count of observations by outcome label
groupcount(outcome, decisions) |> sort

3-element Dictionary{Outcome, Int64}
           Outcome(1, "annulled") │ 236
    Outcome(0, "claim dismissed") │ 295
 Outcome(1, "partially annulled") │ 696

Exploratory Results

Code

# Helper function to count and compute the share of 
# dismissed cases for a collection of decisions
function summarize_outcome(ds)
    n = length(ds)
    countf(l) = count(d -> label(outcome(d)) == l, ds) / n
    outcome_labels = ("claim dismissed", "partially annulled", "annulled")
    dismissed, partial, annulled = countf.(outcome_labels)
    (;n, dismissed, partial, annulled)
end;

# yarrrr...
function DataFrames.DataFrame(d::Dictionary)
    ks = collect(keys(d))
    vs = collect(values(d))
    df = DataFrame(vs)
    insertcols!(df, 1, :keys => ks)
end

Is there variation over time?

df = map(summarize_outcome, group(Dates.year ∘ date, decisions)) |> DataFrame;

Plot

Code

using CairoMakie, AlgebraOfGraphics

function plot_outcome_year(df)
    outcomes = [:dismissed, :partial, :annulled]
    dflong = DataFrames.stack(df, outcomes)
    dflong.value = dflong.value .* dflong.n

    plt = data(dflong) * mapping(
            :keys => "", :value => ""; 
            stack=:variable, 
            color=:variable => ""
          ) * visual(BarPlot)
    
    draw(plt; legend=(;position=:bottom))
end

plot_outcome_year(df)

Gaussian process model

using ApproximateGPs
using Distributions
using LinearAlgebra
using LogExpFunctions: logistic, softplus, invsoftplus
using Zygote
using Optim

function build_latent_gp(θ)
    variance, lengthscale = softplus.(θ)
    kernel = variance * with_lengthscale(SqExponentialKernel(), lengthscale)
    LatentGP(GP(kernel), BernoulliLikelihood() , 1e-8)
end

function optimize_hyperparams(make_f, x, y; θ_init=invsoftplus.([1, 0.05]))
    objective = build_laplace_objective(make_f, x, y)
    grad(θ) = only(Zygote.gradient(objective, θ))
    result = Optim.optimize(objective, grad, θ_init, LBFGS(); inplace=false)
    objective, result
end

function posterior_optimize(x, y)
    objective, optimized = optimize_hyperparams(build_latent_gp, x, y)
    lf_opt = build_latent_gp(optimized.minimizer)
    posterior(LaplaceApproximation(;f_init=objective.cache.f), lf_opt(x), y)
end

function simulate()
    X = range(0, 23.5; length=48)
    f(x) = 3 * sin(10 + 0.6x) + sin(0.1x) - 1
    ps = logistic.(f.(X)) 
    Y = [rand(Bernoulli(p)) for p in ps]
    X, Y, f
end

function plot_data!(ax, x, y; true_f = nothing)
    scatter!(ax, x, y)
    !isnothing(true_f) && lines!(ax, x, f)
end
function plot_data(x, y; true_f=nothing)
    fig = Figure()
    ax = Axis(fig[1,1])
    plot_data!(ax, x, y; true_f)
    fig
end

function plot_posterior!(ax, x, y, xgrid, fpost; true_f=nothing)
    fx = fpost(xgrid, 1e-8)
    fsamples = rand(fx, 100)
    foreach(eachcol(fsamples)) do y
        lines!(ax, xgrid, logistic.(y); color=:grey80)
    end
    lines!(ax, xgrid, map(logistic ∘ mean, eachrow(fsamples)); color=:red, linewidth=2)
    plot_data!(ax, x, y; true_f)
end
function plot_posterior(x, y, xgrid, fpost; true_f=nothing)
    fig = Figure()
    ax = Axis(fig[1,1])
    plot_posterior!(ax,x, y, xgrid, fpost; true_f)
    fig
end;

plotting code

x = [Dates.days(date(d) - minimum(date, decisions)) for d in decisions] ./ 365
y = (id ∘ outcome).(decisions)

#post = posterior_optimize(x, y)
make_f = build_latent_gp([1.0, 5.0])
post = posterior(LaplaceApproximation(), make_f(x), y)
plot_posterior(x, y, range(0, maximum(x), 100), post)

Data

20×5 DataFrame

Row	keys	n	dismissed	partial	annulled
	Int64	Int64	Float64	Float64	Float64
1	2000	66	0.272727	0.530303	0.19697
2	2001	68	0.308824	0.544118	0.147059
3	2002	68	0.205882	0.588235	0.205882
4	2003	69	0.318841	0.478261	0.202899
5	2004	69	0.333333	0.521739	0.144928
6	2005	67	0.253731	0.597015	0.149254
7	2007	41	0.341463	0.609756	0.0487805
8	2008	71	0.253521	0.577465	0.169014
9	2009	69	0.304348	0.565217	0.130435
10	2010	69	0.275362	0.594203	0.130435
11	2011	84	0.22619	0.583333	0.190476
12	2012	76	0.223684	0.644737	0.131579
13	2013	53	0.207547	0.584906	0.207547
14	2014	58	0.0517241	0.741379	0.206897
15	2015	51	0.156863	0.529412	0.313725
16	2016	45	0.222222	0.4	0.377778
17	2017	47	0.170213	0.595745	0.234043
18	2018	52	0.211538	0.576923	0.211538
19	2019	55	0.236364	0.490909	0.272727
20	2020	49	0.163265	0.55102	0.285714

Looking at outcomes by year, the most striking feature is missing data for 2006 and partially 2007, which is due to judge names not being recorded in the decision documents for that period. Furthermore, there seem to be fewer cases per year since about 2013. (TODO: Is this in accordance with reporting?) In terms of variation over time, there are slightly less dismissals in recent years.

Is there variation between senates?

df = map(summarize_outcome, group(label ∘ senate, decisions)) |> DataFrame;

Plot

Code

function plot_outcome_senate(df)
    outcomes = [:dismissed, :partial, :annulled]
    dflong = DataFrames.stack(df, outcomes)
    dflong.value = dflong.value .* dflong.n

    plt = data(dflong) * mapping(
            :keys => "", :value => ""; 
            stack=:variable, 
            color=:variable => ""
          ) * visual(BarPlot)
    
    draw(plt; legend=(;position=:bottom), axis=(;xticklabelrotation=1))
end

plot_outcome_senate(df)

Data

8×5 DataFrame

Row	keys	n	dismissed	partial	annulled
	String	Int64	Float64	Float64	Float64
1	1. Senate	169	0.230769	0.56213	0.207101
2	2. Senate	260	0.242308	0.561538	0.196154
3	3. Senate	264	0.212121	0.602273	0.185606
4	4. Senate	297	0.282828	0.622896	0.0942761
5	0. Senate	38	0.263158	0.421053	0.315789
6	5. Senate	119	0.235294	0.504202	0.260504
7	6. Senate	56	0.160714	0.392857	0.446429
8	7. Senate	24	0.25	0.541667	0.208333

There is some slight variation in case outcomes across senates, with about a 7 percentage point difference in dismissal rates between, e.g., senates 3 and 4.

Is there variation between judges?

Code

Model.label(s::String) = s

function flatten_and_summarize(decisions, by)
    df = DataFrame(group=by.(decisions), outcome=label.(outcome.(decisions)))
    df = @chain df begin
        DataFrames.flatten(:group)
        groupby([:group, :outcome])
        combine(nrow => :count)
        groupby(:group)
        @transform(:n = sum(:count))
        @rtransform(:share = :count / :n)
        unstack([:group, :n], :outcome, :share)
        @rtransform(:group = label(:group))
        sort!(:n; rev=true)
    end
end;

df = flatten_and_summarize(decisions, judges);

Plot

Code

function plot_outcome_judge(df)
    outcomes = ["claim dismissed" => "dismissed", 
                "partially annulled" => "partial", 
                "annulled" => "annulled"]

    dftop = first(rename(df, outcomes), 15)
    dflong = DataFrames.stack(dftop, last.(outcomes))
    dflong.counts = dflong.value .* dflong.n
    
    judgeorder = sort!(dftop, :dismissed).group

    plt = data(dflong) * mapping(
            :group => sorter(judgeorder) => "", :counts => ""; 
            stack=:variable, 
            color=:variable => ""
          ) * visual(BarPlot)
    
    draw(plt; legend=(;position=:bottom), axis=(;xticklabelrotation=1))
end

plot_outcome_judge(df)

Data

200×5 DataFrame

175 rows omitted

Row	group	n	partially annulled	claim dismissed	annulled
	String	Int64	Float64?	Float64?	Float64?
1	Voit	171	0.561404	0.315789	0.122807
2	Gutermuth	162	0.512346	0.290123	0.197531
3	Schuster	145	0.503448	0.317241	0.17931
4	Sredl	144	0.597222	0.173611	0.229167
5	Engels	123	0.682927	0.211382	0.105691
6	Schramm	109	0.678899	0.201835	0.119266
7	Müller	107	0.626168	0.242991	0.130841
8	Rauch	105	0.533333	0.333333	0.133333
9	Martens	101	0.524752	0.247525	0.227723
10	Brandt	100	0.64	0.17	0.19
11	Friehe	93	0.505376	0.225806	0.268817
12	Huber	86	0.430233	0.418605	0.151163
13	Guth	80	0.6625	0.1625	0.175
⋮	⋮	⋮	⋮	⋮	⋮
189	Pagenberg	1	1.0	missing	missing
190	Kraft	1	missing	1.0	missing
191	Sekretaruk	1	1.0	missing	missing
192	Fink	1	1.0	missing	missing
193	Albrecht	1	1.0	missing	missing
194	Staudenmaier	1	1.0	missing	missing
195	S_Schmidt	1	missing	missing	1.0
196	Kruppa	1	1.0	missing	missing
197	Seyfarth	1	missing	1.0	missing
198	Peters	1	1.0	missing	missing
199	Philipps	1	1.0	missing	missing
200	Maierbacher	1	1.0	missing	missing

Looking at some of the most active judges, there are relatively large differences in dissmissal rates; Consider as extreme cases e.g. Guth, with only 16% dismissals out of 80 cases vs. Huber, with about 42% dismissals out of 86 cases.

Is there variation betweeen technologies?

df = flatten_and_summarize(decisions, class ∘ patent);

Plot

Code

function plot_outcome_judge(df)
    outcomes = ["claim dismissed" => "dismissed", 
                "partially annulled" => "partial", 
                "annulled" => "annulled"]

    dftop = first(rename(@rsubset(df, :group != "Y10"), outcomes), 15)
    dflong = DataFrames.stack(dftop, last.(outcomes))
    dflong.counts = dflong.value .* dflong.n
    
    order = sort!(dftop, :dismissed).group

    plt = data(dflong) * mapping(
            :group => sorter(order) => "", :counts => ""; 
            stack=:variable, 
            color=:variable => ""
          ) * visual(BarPlot)
    
    draw(plt; legend=(;position=:bottom), axis=(;xticklabelrotation=1))
end

plot_outcome_judge(df)

Data

116×5 DataFrame

91 rows omitted

Row	group	n	partially annulled	claim dismissed	annulled
	String	Int64	Float64?	Float64?	Float64?
1	Y10	183	0.535519	0.240437	0.224044
2	A61	132	0.719697	0.159091	0.121212
3	H04	116	0.568966	0.112069	0.318966
4	G01	82	0.658537	0.182927	0.158537
5	H01	73	0.520548	0.328767	0.150685
6	B65	69	0.565217	0.202899	0.231884
7	B60	66	0.575758	0.212121	0.212121
8	B29	62	0.435484	0.370968	0.193548
9	F16	61	0.508197	0.245902	0.245902
10	Y02	59	0.542373	0.186441	0.271186
11	G06	56	0.75	0.0714286	0.178571
12	B01	46	0.543478	0.195652	0.26087
13	E04	42	0.547619	0.309524	0.142857
⋮	⋮	⋮	⋮	⋮	⋮
105	D05	1	missing	1.0	missing
106	C10	1	1.0	missing	missing
107	B43	1	missing	1.0	missing
108	F27	1	1.0	missing	missing
109	C11	1	missing	missing	1.0
110	A46	1	missing	1.0	missing
111	A44	1	missing	1.0	missing
112	B04	1	1.0	missing	missing
113	C40	1	1.0	missing	missing
114	B81	1	1.0	missing	missing
115	F26	1	1.0	missing	missing
116	A42	1	1.0	missing	missing

There is some evidence for variation in outcome across different technologies; compare, e.g., G06 (computing, calculating, counting), with a 7% dismissal rate, and B29 (working of plastics), with a 37% dismissal rate.

Is there variation across panel compositions?

df = sort!(map(summarize_outcome, group(decisions) do ds
    js = label.(first(judges(ds), 3))
    js = join(Set(js), ", ")
end); rev=true) |> DataFrame;

Plot

Code

function plot_outcome_composition(df)
    outcomes = [:dismissed, :partial, :annulled]
    dflong = first(df, 15)
    dflong = DataFrames.stack(dflong, outcomes)
    dflong.value = dflong.value .* dflong.n

    plt = data(dflong) * mapping(
            :keys => "", :value => ""; 
            stack=:variable, 
            color=:variable => ""
          ) * visual(BarPlot)
    
    draw(plt; 
         legend=(;position=:bottom), 
         axis=(;xticklabelrotation=1.4),
         figure=(;resolution=(700, 700)))
end

plot_outcome_composition(df)

Data

523×5 DataFrame

498 rows omitted

Row	keys	n	dismissed	partial	annulled
	String	Int64	Float64	Float64	Float64
1	Frowein, Landfermann, Barton	29	0.37931	0.448276	0.172414
2	Voit, Martens, Albertshofer	18	0.277778	0.5	0.222222
3	Gutermuth, Meinhardt, Henkel	16	0.375	0.5625	0.0625
4	Merzbach, Fritze, Sredl	16	0.3125	0.4375	0.25
5	Guth, Schramm, Proksch-Ledig	15	0.266667	0.666667	0.0666667
6	Pösentrup, Hellebrand, Köhn	15	0.133333	0.533333	0.333333
7	Schermer, Engels, Proksch-Ledig	14	0.285714	0.5	0.214286
8	Egerer, Guth, Schramm	12	0.0	0.666667	0.333333
9	Schwendy, Kalkoff, Obermayer	11	0.363636	0.636364	0.0
10	Schwendy, Klosterhuber, Haaß	11	0.272727	0.636364	0.0909091
11	Merzbach, Sredl, Lokys	11	0.272727	0.545455	0.181818
12	Voit, Winkler, Huber	10	0.8	0.2	0.0
13	Brandt, Hellebrand, Wagner	10	0.3	0.5	0.2
⋮	⋮	⋮	⋮	⋮	⋮
512	Bork, Schwarz, Kopacek	1	0.0	0.0	1.0
513	Klante, Musiol, Schwarz	1	0.0	0.0	1.0
514	Voit, Martens, Müller	1	0.0	0.0	1.0
515	Scholz, Martens, Bayer	1	0.0	0.0	1.0
516	Merzbach, Gottstein, Martens	1	0.0	0.0	1.0
517	Geier, Baumgart, Sandkämper	1	0.0	0.0	1.0
518	Forkel, Hartlieb, Thum-Rung	1	0.0	0.0	1.0
519	Fritze, Wiegele, Hermann	1	0.0	0.0	1.0
520	Hartlieb, Friedrich, Grote-Bittner	1	0.0	0.0	1.0
521	Zimmerer, Werner, Friehe	1	0.0	0.0	1.0
522	Rauch, Thum-Rung, Schnurr	1	0.0	0.0	1.0
523	Rauch, Fritze, Schnurr	1	0.0	0.0	1.0

In general, this is hard to answer with the data at hand because we don’t observe individual decisions but only the collective outcome; The best we can do is look at distinct combinations of judges. When picking only the first three judges for each case and looking for all cases handled by this trio, some differences in decision outcomes become visible. However, case numbers are generally too low for conclusive results.

Does a judge’s decision making change over time?

Plots

using Dates

function plot_judges(decisions, js)
    fig = Figure(resolution=(700, 1000))
    lay = CartesianIndices((5,2))

    for (i, j) in enumerate(js)
        ds = filter(decisions) do d
            label(first(judges(d))) == j
        end

        x = [Dates.days(date(d) - minimum(date, ds)) for d in ds] ./ 365
        y = (id ∘ outcome).(ds)

        #post = posterior_optimize(x, y)
        make_f = build_latent_gp([1.0, 4.0])
        post = posterior(LaplaceApproximation(), make_f(x), y)
        
        ax = Axis(fig[Tuple(lay[i])...]; title = j)
        plot_posterior!(ax, x, y, range(0, maximum(x), 100), post)
    end

    fig
end

js = [
        "Voit", "Schuster", 
        "Gutermuth", "Schramm", 
        "Engels", "Sredl",
        "Meinhardt", "Hellebrand", 
        "Schwendy", "Rauch"
     ]

plot_judges(decisions, js)

While for some judges there seems to be a tendency towards an increased nullification rate over their activity period (e.g, Gutermuth, Engels, Meinhardt), this is not a general trend and statistically quite uncertain.

Is there still variation between judges when controlling for the other factors?

Code

using Arrow

model = MixedMembershipModel(decisions)

post_stored = "../data/derivative/inference/posterior_mixed_membership.arrow"
samples = if isfile(post_stored)
    Arrow.Table(post_stored) |> DataFrame
else
    post = Model.sample(model, 1000, 4)
    ess, rhat, treestats = Model.checkconvergence(post)
    println(ess); println(rhat); println(treestats)
    Model._post(post)
end

function plot_mixed_membership_model(model, samples, decisions)
    empirical_mean = mean(id ∘ outcome, decisions)

    fig = Figure(resolution=(700, 1000))

    ax_pred = Axis(fig[1,1:3]; title="Probability of (partial) annullment by case")
    ax_time = Axis(fig[2,1:3]; title="Year effects")
    ax_tech = Axis(fig[3,1:3]; title="Technology effects")
    ax_juds = Axis(fig[4,1:3]; title="Judge effects")

    Model.errorplot!(ax_pred, Prediction(), model, samples; sort=true); hlines!(ax_pred, empirical_mean; color = :red)
    Model.errorplot!(ax_time, Effect(), samples.zt .* samples.σt; sigma=1)
    Model.errorplot!(ax_tech, Effect(), samples.zc .* samples.σc; sigma=1)
    Model.errorplot!(ax_juds, Effect(), samples.zj .* samples.σj; sigma=1)

    axt = Axis(fig[5,1]; title="Year effect s.d.")
    axc = Axis(fig[5,2]; title="Technology effect s.d.")
    axj = Axis(fig[5,3]; title="Judge effect s.d.")

    density!(axt, samples.σt; color=:grey80)
    density!(axc, samples.σc; color=:grey80)
    density!(axj, samples.σj; color=:grey80)

    fig
end

plot_mixed_membership_model(model, samples, decisions)

In a mixed membership model controlling for time and patent technology, judge effects persist. Using the standard deviation of the varying judge effects as a test quantity, we would reject the null hypothesis corresponding to the legalist model of decision making.